#006 Asset Allocation with Random Portfolio Weights¶

In this code, we'll define a function to generate a random portfolio weights, perform an asset allocation, and then and analyze their returns.

libs¶

Install Libs. (remove comments '#' if need to install the libraries)

In [1]:
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install numpy
#    !pip install plotly_express
#    !pip install random
In [2]:
#import Libraries
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import random
import plotly.graph_objects as go

Functions¶

functions already defined that we use on this code

In [3]:
# Define a function using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
        #fig.update_traces(line_width = 3)
        fig.update_layout(legend=dict(orientation="h",)) 
        
    fig.show()
    
# Define a function using Plotly Express, changes axis y to logarithm scale
def log_plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    
    for i in df.columns[0:]:
        # Add range slider
        #fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df.index, y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
        #fig.update_traces(line_width = 3)
        fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.update_yaxes(type="log")
    fig.show()
    
# Define a function using Plotly Express, changes axis y to logarithm scale
def plotly_line(df, y, title):  
    
    # Create figure
    fig = go.Figure()
    fig.update_layout(title_text = title) 
    fig.add_scatter(x = df.index, y = y)
    # Update Layout
    fig.update_layout({'plot_bgcolor': "white"})
    #fig.update_traces(line_width = 3)
    fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.show()    
        
# Define a function using Plotly Express, changes axis y to logarithm scale
def log_plotly_line(df, y, title):  
    
    # Create figure
    fig = go.Figure()
    fig.update_layout(title_text = title) 
    fig.add_scatter(x = df.index, y = y)
    # Update Layout
    fig.update_layout({'plot_bgcolor': "white"})
    #fig.update_traces(line_width = 3)
    fig.update_layout(legend=dict(orientation="h",)) 
    
    #changes y to logarithm scale
    fig.update_yaxes(type="log")
    fig.show()
In [4]:
# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[0:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df

6.1 Equal Weighted Portfolio¶

In [5]:
file_name = input('Input the CSV file name: ')
initial_investment = int(input('Input the initial investment: '))
n_runs = int(input('Input the number of simulations: '))

## BRL5
#      PETR4.SA VALE3.SA TAEE11.SA ITSA4.SA WEGE3.SA

## MAG7
#      AAPL AMZN GOOG META MSFT NVDA TSLA

## BRL_top10 
#      CRFB3.SA CPLE6.SA ECOR3.SA ITUB4.SA JBSS3.SA RENT3.SA PRIO3.SA SBSP3.SA VALE3.SA VIVT3.SA

Input the CSV file name: BRL10
Input the initial investment: 1000000
Input the number of simulations: 5
In [6]:
#read CSV file
Stock_Prices_df = pd.read_csv(file_name)
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
Stock_Prices_df.set_index(['Date'], inplace = True)
In [7]:
#obtain weights vector
n_assets = len(Stock_Prices_df.columns)
#lock vector to test function
weights = np.ones(n_assets) * 1/n_assets
weights
Out[7]:
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
In [8]:
#Define a function that performs an Asset Allocation
def asset_allocation(df, initial_investment, weights):
    ''' Performs an asset Allocation for a given DF, initial investment value and weights'''
    
    portfolio_df = df.copy()
    
    # Scale stock prices using the "price_scaling"
    scaled_df = price_scaling(df)
    
    #enumerate method links Stocks tickers in columns along with a counter position weight (i), like an index
    for i, stock in enumerate(scaled_df):
        portfolio_df[stock] = weights[i] * scaled_df[stock]  * initial_investment
    
    # Sum up all values and place the result in a new column titled "portfolio value [$]" 
    portfolio_df['Total Value [$]'] = portfolio_df.sum(axis = 1, numeric_only = True)
    
    # Calculate the portfolio percentage daily return and replace NaNs with zeros
    portfolio_df['Daily Return [%]'] = portfolio_df['Total Value [$]'].pct_change(1) * 100 
    portfolio_df.replace(np.nan, 0, inplace = True)
    
    return portfolio_df
In [9]:
#Asset Allocation with parameters defined
   
portfolio_df = asset_allocation(Stock_Prices_df, initial_investment, weights)
Eqw = portfolio_df
portfolio_df.round(2)
Out[9]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 100000.00 100000.00 100000.00 100000.00 100000.00 100000.00 100000.00 100000.00 100000.00 100000.00 1000000.00 0.00
2018-12-17 99692.31 99555.55 97731.96 97325.43 98509.95 104978.13 97950.53 98694.52 100727.19 99056.60 994222.16 -0.58
2018-12-18 99384.62 100833.31 97422.68 98904.25 98675.50 98269.33 97597.19 98074.41 101100.60 100856.98 991118.87 -0.31
2018-12-19 96553.83 97333.32 96907.21 97580.61 95529.80 99183.28 98339.21 97225.86 98290.08 98575.67 975518.87 -1.57
2018-12-20 96461.52 100333.34 97113.40 97552.45 94370.86 96246.96 99346.72 99869.46 97897.01 97278.95 976470.66 0.10
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 469772.65 69389.34 95695.89 108459.32 247430.61 2062226.53 244892.92 248979.82 209180.82 181340.74 3937368.64 1.12
2023-12-08 470264.01 67795.60 95904.60 109831.35 250366.22 2165775.45 243260.03 247953.70 209757.09 182260.56 4043168.61 2.69
2023-12-11 468298.44 67979.49 97574.33 109076.73 249108.11 2172095.38 243459.16 252681.14 209929.96 180250.58 4050453.32 0.18
2023-12-12 469772.65 69450.64 96426.39 108527.91 247430.61 2129314.58 241029.74 253707.28 210304.52 181783.62 4007747.96 -1.05
2023-12-13 474195.16 70002.33 98096.11 110140.06 249632.31 2163830.82 245888.58 260377.04 209238.46 182839.70 4064240.57 1.41

1240 rows × 12 columns

In [10]:
#Plot data:

plotly_line(portfolio_df, portfolio_df['Total Value [$]'], "Portfolio Total Value")
log_plotly_line(portfolio_df, portfolio_df['Total Value [$]'], "Portfolio Total Value - Log Scale")
plotly_line(portfolio_df, portfolio_df['Daily Return [%]'], "Daily Return [%]")
In [11]:
plotly_data(portfolio_df, "Equal Weighted Porfolio")
log_plotly_data(portfolio_df, "Equal Weighted Porfolio")

6.2 Random Weighted Portfolio¶

6.2.1 Define a function to generate random weights¶

In [12]:
def rand_weights(n):
    ''' Produces n random weights that sum to 1 '''
    k = np.random.rand(n)
    return k / sum(k)
In [13]:
#obtain weights vector
n_assets = len(Stock_Prices_df.columns)

weights = rand_weights(n_assets).round(4)
display(weights)
sum(weights)

array([0.1531, 0.1587, 0.1102, 0.128 , 0.1685, 0.0648, 0.0701, 0.0239,
       0.0441, 0.0787])
Out[13]:
1.0001

6.2.2 Asset Allocation Auto¶

In [14]:
#Eqw     --  Equal Weighted </p>
#Rdw_1 -- Random Weighted 1 </p>
#Rdw_2 -- Random Weighted 2 [...]
In [15]:
def random_port_generate(initial_investment, n_runs):
    #obtain weights vector
    n_assets = len(Stock_Prices_df.columns)
    #lock vector to test function
    eq_weights = np.ones(n_assets) * 1/n_assets
    #Asset Allocation with parameters defined
    Eqw_df = asset_allocation(Stock_Prices_df, initial_investment, eq_weights)[['Total Value [$]', 'Daily Return [%]']]
    Eqw_df = Eqw_df.rename({'Total Value [$]':'Eqw [$]', 'Daily Return [%]':'Eqw [%]'}, axis="columns")
    All_df = Eqw_df
    
    # Placeholder to store all weights
    weights_runs = np.zeros((n_runs, n_assets))
    
    for i in range(n_runs):
        # Generate random weights 
        weights = rand_weights(n_assets)
        # Store the weights
        weights_runs[i,:] = weights
        # Random Asset Allocation
        df = asset_allocation(Stock_Prices_df, initial_investment, weights)[['Total Value [$]', 'Daily Return [%]']]
        #rename columns for iterate
        Rdw = df.rename({'Total Value [$]':'Rdw_{}[$]'.format(i), 'Daily Return [%]':'Rdw_{}[%]'.format(i)}, axis="columns")

        All_df = pd.merge(All_df, Rdw, on = 'Date')

        #All_df = Eqw_df.join(Rdw)

        print("Simulation Run = {}".format(i))   
        print("Weights = {}".format(weights_runs[i].round(3))) 
        print('\n')
        
    return All_df
In [16]:
df = random_port_generate(initial_investment, n_runs)
daily_returns_df = df.iloc[:, 1::2] 
total_values_df = df.iloc[:, 0::2] 
display(df.round(2))

Simulation Run = 0
Weights = [0.156 0.062 0.091 0.123 0.072 0.061 0.106 0.174 0.066 0.088]


Simulation Run = 1
Weights = [0.024 0.136 0.017 0.112 0.116 0.08  0.117 0.057 0.141 0.201]


Simulation Run = 2
Weights = [0.078 0.105 0.131 0.147 0.065 0.114 0.091 0.101 0.024 0.144]


Simulation Run = 3
Weights = [0.151 0.2   0.093 0.003 0.095 0.121 0.006 0.188 0.08  0.064]


Simulation Run = 4
Weights = [0.21  0.115 0.15  0.022 0.213 0.109 0.136 0.002 0.001 0.043]
Eqw [$] Eqw [%] Rdw_0[$] Rdw_0[%] Rdw_1[$] Rdw_1[%] Rdw_2[$] Rdw_2[%] Rdw_3[$] Rdw_3[%] Rdw_4[$] Rdw_4[%]
Date
2018-12-14 1000000.00 0.00 1000000.00 0.00 1000000.00 0.00 1000000.00 0.00 1000000.00 0.00 1000000.00 0.00
2018-12-17 994222.16 -0.58 991063.47 -0.89 994194.03 -0.58 992720.18 -0.73 998482.59 -0.15 993906.60 -0.61
2018-12-18 991118.87 -0.31 989416.60 -0.17 995783.35 0.16 989950.25 -0.28 992621.07 -0.59 987930.28 -0.60
2018-12-19 975518.87 -1.57 974476.14 -1.51 977692.86 -1.82 976290.79 -1.38 973708.63 -1.91 971199.50 -1.69
2018-12-20 976470.66 0.10 977968.07 0.36 977568.14 -0.01 977108.06 0.08 978980.54 0.54 969928.60 -0.13
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 3937368.64 1.12 3430337.24 0.69 3366822.39 1.19 4019861.48 1.21 4437702.59 1.09 4427398.64 1.16
2023-12-08 4043168.61 2.69 3495232.75 1.89 3452606.75 2.55 4139779.89 2.98 4562664.05 2.82 4544646.04 2.65
2023-12-11 4050453.32 0.18 3502628.28 0.21 3454515.61 0.06 4147978.48 0.20 4575804.73 0.29 4546761.97 0.05
2023-12-12 4007747.96 -1.05 3477492.10 -0.72 3421319.83 -0.96 4099627.08 -1.17 4529579.15 -1.01 4496813.88 -1.10
2023-12-13 4064240.57 1.41 3527971.85 1.45 3465428.92 1.29 4161383.29 1.51 4595496.62 1.46 4559113.39 1.39

1240 rows × 12 columns

6.2.2.1 Plotting Data¶

In [17]:
plotly_data(total_values_df, "Portfolios Total Value[$]")
log_plotly_data(total_values_df, "Portfolios Total Value[$]")
plotly_data(daily_returns_df, "Portfolio Daily Returns [%]")
In [18]:
import plotly.express as px
# Plot histograms for stocks daily returns using plotly express
fig = px.histogram(daily_returns_df)
fig.update_layout({'plot_bgcolor': "white"})

6.2.3 Asset Allocation Manual¶

In [19]:
#Random Asset Allocation 01
weights_1 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_1))

Rdw_1 = asset_allocation(Stock_Prices_df, initial_investment, weights_1)
Rdw_1.round(2)

Value Invested: $ 1000000
Number of Stocks: 10
Weights: [0.1514 0.0687 0.0459 0.0808 0.0807 0.1477 0.1912 0.122  0.1059 0.0058]
Out[19]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 151400.00 68700.00 45900.00 80800.00 80700.00 147700.00 191200.00 122000.00 105900.00 5800.00 1000100.00 0.00
2018-12-17 150934.16 68394.66 44858.97 78638.95 79497.53 155052.69 187281.42 120407.31 106670.09 5745.28 997481.07 -0.26
2018-12-18 150468.31 69272.49 44717.01 79914.64 79631.13 145143.80 186605.82 119650.78 107065.54 5849.70 988319.21 -0.92
2018-12-19 146182.50 66867.99 44480.41 78845.13 77092.55 146493.70 188024.58 118615.54 104089.19 5717.39 976408.99 -1.21
2018-12-20 146042.75 68929.01 44575.05 78822.38 76157.28 142156.77 189950.93 121840.74 103672.93 5642.18 977790.00 0.14
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 711235.79 47670.48 43924.41 87635.13 199676.50 3045908.59 468235.26 303755.37 221522.49 10517.76 5140081.79 1.39
2023-12-08 711979.71 46575.58 44020.21 88743.73 202045.54 3198850.33 465113.18 302503.52 222132.76 10571.11 5292535.67 2.97
2023-12-11 709003.83 46701.91 44786.62 88133.99 201030.24 3208184.88 465493.92 308271.00 222315.83 10454.53 5304376.75 0.22
2023-12-12 711235.79 47712.59 44259.71 87690.55 199676.50 3144997.63 460848.87 309522.89 222712.49 10543.45 5239200.48 -1.23
2023-12-13 717931.47 48091.60 45026.12 88993.17 201453.28 3195978.12 470138.96 317659.99 221583.53 10604.70 5317460.93 1.49

1240 rows × 12 columns

In [20]:
#Random Asset Allocation 02
weights_2 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_2))

Rdw_2 = asset_allocation(Stock_Prices_df, initial_investment, weights_2)
Rdw_2.round(2)

Value Invested: $ 1000000
Number of Stocks: 10
Weights: [0.2244 0.2137 0.148  0.0746 0.0101 0.116  0.018  0.0359 0.0021 0.1572]
Out[20]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 224400.00 213700.00 148000.00 74600.00 10100.00 116000.00 18000.00 35900.00 2100.00 157200.00 1000000.00 0.00
2018-12-17 223709.54 212750.22 144643.30 72604.77 9949.51 121774.63 17631.10 35431.33 2115.27 155716.97 996326.63 -0.37
2018-12-18 223019.08 215480.79 144185.57 73782.57 9966.23 113992.42 17567.49 35208.71 2123.11 158547.17 993873.15 -0.25
2018-12-19 216666.80 208001.31 143422.67 72795.13 9648.51 115052.60 17701.06 34904.08 2064.09 154960.95 975217.21 -1.88
2018-12-20 216459.66 214412.35 143727.83 72774.13 9531.46 111646.48 17882.41 35853.14 2055.84 152922.50 977265.79 0.21
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 1054169.83 148285.03 141629.92 80910.65 24990.49 2392182.78 44080.72 89383.75 4392.80 285067.64 4265093.62 1.16
2023-12-08 1055272.44 144879.20 141938.81 81934.19 25286.99 2512299.52 43786.81 89015.38 4404.90 286513.60 4385331.82 2.82
2023-12-11 1050861.69 145272.18 144410.01 81371.24 25159.92 2519630.64 43822.65 90712.53 4408.53 283353.92 4389003.30 0.08
2023-12-12 1054169.83 148416.03 142711.06 80961.82 24990.49 2470004.91 43385.35 91080.91 4416.39 285763.85 4345900.66 -0.98
2023-12-13 1064093.93 149594.97 145182.25 82164.48 25212.86 2510043.75 44259.94 93475.36 4394.01 287424.01 4405845.57 1.38

1240 rows × 12 columns

In [21]:
#Random Asset Allocation 03
weights_3 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_3))

Rdw_3 = asset_allocation(Stock_Prices_df, initial_investment, weights_3)
Rdw_3.round(2)

Value Invested: $ 1000000
Number of Stocks: 10
Weights: [0.0405 0.0588 0.0667 0.0805 0.1221 0.006  0.2278 0.1794 0.1887 0.0295]
Out[21]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 40500.00 58800.00 66700.00 80500.00 122100.00 6000.00 227800.00 179400.00 188700.00 29500.00 1000000.00 0.00
2018-12-17 40375.39 58538.67 65187.22 78346.97 120280.65 6298.69 223131.32 177057.96 190072.21 29221.70 988510.76 -1.15
2018-12-18 40250.77 59289.99 64980.93 79617.92 120482.78 5896.16 222326.39 175945.49 190776.84 29752.81 989320.08 0.08
2018-12-19 39104.30 57231.99 64637.11 78552.39 116641.89 5951.00 224016.73 174423.19 185473.37 29079.82 975111.79 -1.44
2018-12-20 39066.92 58996.00 64774.64 78529.72 115226.82 5774.82 226311.83 179165.81 184731.66 28697.29 981275.49 0.63
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 190257.92 40800.93 63829.16 87309.75 302112.78 123733.59 557866.06 446669.79 394724.21 53495.52 2260799.72 -0.00
2023-12-08 190456.92 39863.81 63968.37 88414.24 305697.15 129946.53 554146.35 444828.94 395811.63 53766.87 2266900.81 0.27
2023-12-11 189660.87 39971.94 65082.08 87806.76 304161.00 130325.72 554599.97 453309.97 396137.84 53173.92 2274230.07 0.32
2023-12-12 190257.92 40836.98 64316.40 87364.97 302112.78 127758.87 549065.76 455150.87 396844.63 53626.17 2267335.35 -0.30
2023-12-13 192049.04 41161.37 65430.11 88662.75 304801.06 129829.85 560134.18 467116.41 394832.97 53937.71 2297955.44 1.35

1240 rows × 12 columns

In [22]:
#Random Asset Allocation 04
weights_4 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_4))

Rdw_4 = asset_allocation(Stock_Prices_df, initial_investment, weights_4)
Rdw_4.round(2)

Value Invested: $ 1000000
Number of Stocks: 10
Weights: [0.0016 0.1769 0.1788 0.002  0.1117 0.0768 0.1183 0.076  0.1346 0.1233]
Out[22]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 1600.00 176900.00 178800.00 2000.00 111700.00 76800.00 118300.00 76000.00 134600.00 123300.00 1000000.00 0.00
2018-12-17 1595.08 176113.77 174744.74 1946.51 110035.61 80623.20 115875.48 75007.83 135578.80 122136.79 993657.81 -0.63
2018-12-18 1590.15 178374.13 174191.75 1978.09 110220.53 75470.84 115457.47 74536.55 136081.41 124356.66 992257.59 -0.14
2018-12-19 1544.86 172182.65 173270.09 1951.61 106706.79 76172.76 116335.29 73891.65 132298.44 121543.80 975897.94 -1.65
2018-12-20 1543.38 177489.68 173638.76 1951.05 105412.25 73917.67 117527.17 75900.79 131769.37 119944.94 979095.06 0.33
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 7516.36 122749.75 171104.26 2169.19 276379.99 1583789.98 289708.32 189224.66 281557.39 223593.13 3147793.02 0.90
2023-12-08 7524.22 119930.42 171477.43 2196.63 279659.06 1663315.54 287776.62 188444.81 282333.04 224727.27 3227385.05 2.53
2023-12-11 7492.77 120255.72 174462.91 2181.53 278253.75 1668169.25 288012.19 192037.67 282565.73 222248.97 3235680.50 0.26
2023-12-12 7516.36 122858.19 172410.39 2170.56 276379.99 1635313.60 285138.19 192817.54 283069.88 224139.20 3201813.90 -1.05
2023-12-13 7587.12 123834.12 175395.85 2202.80 278839.30 1661822.07 290886.19 197886.55 281634.97 225441.35 3245530.31 1.37

1240 rows × 12 columns

In [23]:
#Random Asset Allocation 02
weights_5 = rand_weights(len(Stock_Prices_df.columns)).round(4)

print("Value Invested: $ {}".format(initial_investment))
print("Number of Stocks: {}".format(len(Stock_Prices_df.columns)))
print("Weights: {}".format(weights_5))

Rdw_5 = asset_allocation(Stock_Prices_df, initial_investment, weights_5)
Rdw_5.round(2)

Value Invested: $ 1000000
Number of Stocks: 10
Weights: [0.0852 0.0579 0.1741 0.1375 0.1459 0.0323 0.1367 0.0509 0.1212 0.0584]
Out[23]:
CPLE6.SA CRFB3.SA ECOR3.SA ITUB4.SA JBSS3.SA PRIO3.SA RENT3.SA SBSP3.SA VALE3.SA VIVT3.SA Total Value [$] Daily Return [%]
Date
2018-12-14 85200.00 57900.00 174100.00 137500.00 145900.00 32300.00 136700.00 50900.00 121200.00 58400.00 1000100.00 0.00
2018-12-17 84937.85 57642.67 170151.34 133822.46 143726.02 33907.93 133898.38 50235.51 122081.35 57849.05 988252.56 -1.18
2018-12-18 84675.70 58382.49 169612.88 135993.35 143967.55 31740.99 133415.35 49919.88 122533.93 58900.48 989142.59 0.09
2018-12-19 82263.86 56355.99 168715.45 134173.33 139377.98 32036.20 134429.71 49487.96 119127.57 57568.19 973536.25 -1.58
2018-12-20 82185.22 58093.00 169074.43 134134.61 137687.08 31087.77 135806.96 50833.55 118651.17 56810.90 974364.72 0.09
... ... ... ... ... ... ... ... ... ... ... ... ...
2023-12-07 400246.30 40176.43 166606.55 149131.56 361001.26 666099.17 334768.62 126730.73 253527.16 105902.99 2604190.76 0.45
2023-12-08 400664.94 39253.65 166969.91 151018.11 365284.31 699545.47 332536.46 126208.44 254225.59 106440.17 2642147.05 1.46
2023-12-11 398990.27 39360.13 169876.91 149980.50 363448.73 701586.81 332808.67 128614.70 254435.11 105266.34 2644368.17 0.08
2023-12-12 400246.30 40211.92 167878.35 149225.88 361001.26 687768.61 329487.66 129137.01 254889.08 106161.63 2626007.70 -0.69
2023-12-13 404014.27 40531.35 170785.33 151442.58 364213.55 698917.36 336129.69 132531.91 253597.01 106778.38 2658941.43 1.25

1240 rows × 12 columns

6.2.3.1 Organize and Plot Data¶

In [24]:
rand_value_df = pd.concat([  Eqw['Total Value [$]'], 
                           Rdw_1['Total Value [$]'], 
                           Rdw_2['Total Value [$]'], 
                           Rdw_3['Total Value [$]'], 
                           Rdw_4['Total Value [$]'], 
                           Rdw_5['Total Value [$]']
                           ], axis=1, join='inner').round(2)
#rand_value_df.to_csv('rand_value_df')
In [25]:
#read CSV file
rand_value_df = pd.read_csv('rand_value')
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
rand_value_df.set_index(['Date'], inplace = True)

log_plotly_data(rand_value_df, "Total Value [$]")
In [26]:
rand_daily_df = pd.concat([  Eqw['Daily Return [%]'], 
                           Rdw_1['Daily Return [%]'], 
                           Rdw_2['Daily Return [%]'], 
                           Rdw_3['Daily Return [%]'], 
                           Rdw_4['Daily Return [%]'], 
                           Rdw_5['Daily Return [%]']
                           ], axis=1, join='inner').round(2)
#rand_daily_df.to_csv('rand_daily')
In [27]:
#read CSV file
rand_daily_df = pd.read_csv('rand_daily')
#The code imports a DataFrame with num index [1,2,3...], this line replace the colum Date to Index
rand_daily_df.set_index(['Date'], inplace = True)

plotly_data(rand_daily_df, "Total Value [$]")